Metabolites, Vol. 15, Pages 783: Untargeted Metabolomics Reveals Distinct Soil Metabolic Profiles Across Land Management Practices

Metabolites doi: 10.3390/metabo15120783

Authors:
Zane A. Vickery
Hector F. Castro
Stephen P. Dearth
Eric D. Tague
Aimée T. Classen
Jessica A. Moore
Michael S. Strickland
Shawn R. Campagna

Background/Objectives: Land management practices strongly influence soil biochemical processes, yet conventional soil measurements often overlook dynamic small-molecule variation underlying nutrient cycling and microbial activity. This study aimed to evaluate whether MS1-based untargeted metabolomics can resolve meaningful biochemical differences among soil systems under distinct land management practices. Methods: Soils from six land-use types—conventional cultivation, organic cultivation, pasture, white pine, tulip poplar, and hardwood forest—were analyzed using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Multivariate analyses, including PLS-DA, were performed to evaluate metabolic variation across systems. Both identified metabolites and unknown spectral features (MSI Level 4) were assessed, and biosynthetic class assignment of unknown features was performed using NPClassifier. Results: Metabolic features revealed clear separation between land management systems, demonstrating distinct chemical fingerprints across ecosystems. While conventional elemental ratios (e.g., C/N) showed minimal differentiation, phosphorus-related stoichiometric ratios (C/P and N/P) displayed strong land-use-dependent differences. NPClassifier superclasses highlighted unique chemical patterns, with forest soils enriched in diverse secondary metabolites, cultivated soils characterized by simplified profiles, and pasture soils dominated by microbial membrane lipids and alkaloids. Conclusions: Untargeted MS1-based metabolomics effectively distinguished soil systems under different land-use practices and revealed ecologically meaningful variation even without complete structural identification. This study demonstrates that an MS1-only workflow leveraging unknown spectral features can robustly distinguish soil systems, underscoring their value in untargeted metabolomics analyses.

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Zane A. Vickery www.mdpi.com